6.11 Daily Profiles - Decompose

# change language to English, otherwise weekdays are in local language
Sys.setlocale("LC_TIME", "English")

## [1] "English_United States.1252"

library(plotly)
library(dplyr)
library(lubridate)

# load time series data
df <- read.csv("https://github.com/hslu-ige-laes/edar/raw/master/sampleData/eboBookEleMeter.csv",
               stringsAsFactors=FALSE,
               sep =";")

# rename column names
colnames(df) <- c("timestamp", "meterValue")

df$timestamp <- parse_date_time(df$timestamp,
                                orders = "YmdHMS",
                                tz = "Europe/Zurich")
df$timestamp <- force_tz(df$timestamp, tzone = "UTC")

# uncomment to filter time range if necessary
#df <- df %>% filter(timestamp > "2015-03-01 00:00:00", timestamp < "2015-04-01 00:00:00")

# Fill missing values with NA
grid.df <- data.frame(timestamp = seq(min(df$timestamp, na.rm = TRUE),
                                      max(df$timestamp, na.rm = TRUE),
                                      by = "15 mins"))
df <- merge(df, grid.df, all = TRUE)

# convert steadily counting energy meter value from kWh to power in kW
df <- df %>% 
  mutate(value = (meterValue - lag(meterValue))*4) %>%
  select(-meterValue) %>%
  na.omit()

# remove negative values which occur beause of change summer/winter time
df <- df %>% filter(value >= 0)

# add metadata for later grouping and visualization purposes
df$x <- hour(df$timestamp) + minute(df$timestamp)/60 + second(df$timestamp) / 3600
df$weekday <- weekdays(df$timestamp)
df$weekday <- factor(df$weekday, c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday", "Sunday"))

df <- df %>% mutate(value = ifelse(x == 0.00, NA, df$value))

# Calculate Mean value for all 15 minutes for each weekday
df <- df %>% group_by(weekday, x) %>% mutate(dayTimeMean = mean(value)) %>% ungroup()

# shrink data frame
df <- df %>%
  select(x, weekday, timestamp, dayTimeMean) %>%
  unique() %>%
  na.omit() %>% 
  arrange(weekday, x)

# plot graph with mean values
maxValMean <- max(df$dayTimeMean, na.rm = TRUE)*1.05

df %>% 
  highlight_key(~weekday) %>%
  plot_ly(x=~x,
          y=~dayTimeMean,
          color=~weekday,
          type="scatter",
          mode="lines",
          alpha = 0.25,
          colors = "dodgerblue4",
          text = ~weekday,
          hovertemplate = paste("Time: ", format(df$timestamp, "%H:%M"),
                                "<br>Mean: %{y:.0f}")) %>%
  # workaround with add_trace to have fixed y axis when selecting a dedicated day
  add_trace(x = 0, y = 0, type = "scatter", showlegend = FALSE, opacity=0) %>% 
  add_trace(x = 24, y = maxValMean, type = "scatter", showlegend = FALSE, opacity=0) %>%
  layout(title = "Daily Profiles - Mean",
         showlegend = TRUE,
         xaxis = list(
           title = "Hour of day",
           tickvals = list(0, 3, 6, 9, 12, 15, 18, 21)
         ),
         yaxis = list(
             title = "Power (kW)",
             range = c(0, maxValMean)
         )
  ) %>% 
  highlight(on = "plotly_hover",
            off = "plotly_doubleclick",
            color = "orange",
            opacityDim = 0.7,
            selected = attrs_selected(showlegend = FALSE)) %>% # this hides elements in the legend
  plotly::config(modeBarButtons = list(list("toImage")), displaylogo = FALSE)